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[Commentary] WITH YOUTH SMOKING AT HISTORICAL LOWS, HOW INFLUENTIAL IS MOVIE SMOKING ON UPTAKE?

2009· letter· en· W1538002509 on OpenAlexaboutno aff
Simon Chapman

Bibliographic record

VenueAddiction · 2009
Typeletter
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsHeadlinePsychologyMultitudeYouth smokingAdvertisingSuspectShadow (psychology)Association (psychology)Tobacco controlPublic healthSocial psychologyMedicinePolitical scienceCriminologyLaw

Abstract

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Before young smokers light their first cigarettes, most have seen uncounted thousands of smoking incidents, whether by family, friends, known and unknown smokers in public settings, in magazines, TV, in music lyrics and video clips, online and in movies. Both noticed and unnoticed, recalled and long-forgotten, this dynamic acculturating soup of influence with its multitude of mutually infusing ingredients has long challenged behavioural scientists with reductionist ambitions. Undaunted by the idea that life's rich tapestry cannot be simply picked apart and neatly reassembled like some advanced Ikea project [1], they pursue the grail of modelling the precise role of suspect variables, laying bare those to which policy makers and interventionists might direct their attentions most profitably. Smoking in movies is one such variable which has attracted large-scale attention in the past decade [2], and Sargent & Hanewinkel's report in this issue [3] adds another brick to the wall of evidence which has already fuelled pixellation of movie smoking scenes in Thailand, efforts to ban smoking completely in Indian ‘Bollywood’ movies and calls for the R-rating of almost all smoking scenes in the United States [4]. Repeated studies have demonstrated an association between exposure and smoking uptake, even after controlling for variables such as parental and peer smoking and permissive parenting style (which increases access to R-rated movies where smoking is more common). A 2008 US National Cancer Institute report [2] called this association ‘causal’. Just as tobacco advertising promotes positive associations with smoking, it would seem unarguable that movie smoking also makes a major contribution to the cultural iconography and appeal of smoking. However, supporters of proposals that smoking scenes should trigger R-ratings to reduce exposure to smoking need to understand that under-age access to R- and even X-rated movies is widespread [5, 6] and, with the exponential growth of the internet, expanding rapidly [7]. Teenagers will also, of course, remain exposed to the sight of smoking via many other highly appealing sources: the ‘omelette’ of influences which socialize smoking plainly contains many more ingredients than just movies. Notwithstanding the questionable precision of claims such as ‘52.2% (30.0–67.3) of smoking initiation can be attributed to exposure to smoking in movies’[8], this invites proportionality questions about the proposed policy solutions: would reducing (but by no means eliminating) teen exposure to smoking in movies have much impact? Awkwardly for the core thesis here, ecological evidence from whole populations shows that despite movie smoking being much higher than in past decades (‘Smoking prevalence among contemporary movie characters is approximately 25%, about twice what it was in the 1970s and 1980s') [2], youth smoking prevalence rates are today at all-time lows in the United States [9], Canada [10], Australia [11], England [12] and New Zealand [13], to name just five populations. If movie smoking is as influential on uptake as some argue, it would seem to be a factor struggling to hold its own against many denormalizing influences driving smoking down in both adults and children [14]. Of utmost concern, however, is the assumption that movies are fair game for single-issue health and social regulators. Movies contain many anti-social scenes (crime, cruelty, reckless driving, racism, misogyny, indifference to the environment), yet other than in some authoritarian political or religiously fundamentalist nations, the state does not appropriate cinema as a blatant ideological state apparatus for whatever noble purpose, including promoting health. In January 2009, the Indian High Court upheld a challenge to a government ban on all smoking in movies. One of the judges argued: ‘A cinematographic film must reflect the realities of life. Smoking is a reality of life. It may be undesirable but it exists’. Few smoking scenes in fact show health consequences, but the spectre of compulsory plot lines requiring disease in 50% of smokers, or closing lines explaining that characters X and Y died 15 years later from a smoking-related disease, would be too silly to contemplate. Equally, one much-praised proposal [15] concedes that scenes of real historical smokers such as Churchill and Castro would not attract an R classification, as (presumably) this would air-brush history. However, if a children's movie set in the heavy-smoking 1950s were made today, the same advocates would insist that no character be shown smoking. Apparently air-brushing historical smoking individuals is wrong, but changing whole historical eras is fine. Movie rating systems in some nations already reflect societal judgement about the inappropriateness for young children to be exposed to some levels of sexual expression, nudity, violence and coarse language. Plainly, there is a good deal of gratuitous and possibly tobacco industry-engineered tobacco product placement in movies. I have agreed elsewhere [4] that provisions should therefore be made for ratings panels to take smoking into account as one factor in the overall contextual enterprise of classification, as happens now with judgement about ‘adult concepts’. However, absolutist prohibitions on showing any smoking in any movie seen by children beckon tobacco control back to its moralistic origins and risks widespread ridicule, as people ask reasonable questions about whether a very long list of other risky behaviours should also therefore be kept from children's eyes. None.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.251
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2009
Admission routes1
Has abstractyes

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